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<Journal>
<PublisherName>theaimsjournal</PublisherName>
<JournalTitle>Allana Management Journal of Research, Pune</JournalTitle>
<PISSN> 2581 - 3137 (</PISSN>
<EISSN>) 2231 - 0290 (Print)</EISSN>
<Volume-Issue>Volume 15, Issue 2</Volume-Issue>
<PartNumber/>
<IssueTopic>Multidisciplinary</IssueTopic>
<IssueLanguage>English</IssueLanguage>
<Season>July 2025 - Dec 2025</Season>
<SpecialIssue>N</SpecialIssue>
<SupplementaryIssue>N</SupplementaryIssue>
<IssueOA>Y</IssueOA>
<PubDate>
<Year>2025</Year>
<Month>12</Month>
<Day>25</Day>
</PubDate>
<ArticleType>Financial Management</ArticleType>
<ArticleTitle>ASSESSING THE IMPACT OF ALGORITHMIC TRADING ON INDIAN STOCK MARKET AND STAKEHOLDERS IN THE ERA OF HIGH-FREQUENCY TRADING – AN ISM APPROACH.</ArticleTitle>
<SubTitle/>
<ArticleLanguage>English</ArticleLanguage>
<ArticleOA>Y</ArticleOA>
<FirstPage>54</FirstPage>
<LastPage>61</LastPage>
<AuthorList>
<Author>
<FirstName/>
<LastName>Gorde</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>N</CorrespondingAuthor>
<ORCID/>
</Author>
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<DOI>https://doi.org/10.62223/AMJR.2025.150206</DOI>
<Abstract>Algorithmic trading has rapidly transformed the functioning of modern financial
markets, particularly in emerging economies like India. This study explores how
algorithmic trading influences market performance and stakeholders, offering a
structured framework to understand its growing significance in the Indian stock
market.
Purpose: The major purpose of this research paper was to understand the
relationship between algorithmic trading and its impact on the stakeholders in the
Indian stock market. This paper focuses on various factors of algorithmic trading,
their impact on performance, and how popular it has become in recent times
Design/Methodology/Approach: This research comprises of primary data and
secondary data. In this paper, researchers have also presented a theoretical
framework for understanding and analyzing the use and impact of algorithmic
trading by using Interpretive Structural Modeling (ISM) to develop an interrelationship
that will provide the right direction to researchers for further research.
Findings: Algorithmic trading strategies and big data and artificial intelligence.
Also, Algorithmic trading strategies and high frequency trading are very important
aspects for each other because if we use algorithms for high frequency trading it
would increase liquidity and improve efficiency in trading. By using the ISM
modeling technique, researchers got three levels based on hierarchy. The output
suggested that the variables in the level 2(High Frequency Trading, Big Data and
Artificial intelligence) level 3(Back testing ability, Diversification of trades) are
considered as the most important factors to assess the impact of algorithmic trading
on Indian stock market and stakeholders in the era of high-frequency trading.
Research Limitations/Implications: The study was conducted by using an
interview method and expert opinion was collected from 30 experts from the finance
domain. The study was limited to 30 experts so limited variables we considered for
the ISM model. The application of this in the real world would require some
modifications.
Originality/Value: It__ampersandsign#39;s the first time a conceptual model has been proposed by
researchers for assessing the impact of algorithmic trading on Indian stock market
by using ISM.</Abstract>
<AbstractLanguage>English</AbstractLanguage>
<Keywords>Algorithmic trading, high-frequency trading, trading strategies, back testing, big data, artificial intelligence.</Keywords>
<URLs>
<Abstract>https://theaimsjournal.org/ubijournal-v1copy/journals/abstract.php?article_id=16061&title=ASSESSING THE IMPACT OF ALGORITHMIC TRADING ON INDIAN STOCK MARKET AND STAKEHOLDERS IN THE ERA OF HIGH-FREQUENCY TRADING – AN ISM APPROACH.</Abstract>
</URLs>
<References>
<ReferencesarticleTitle>References</ReferencesarticleTitle>
<ReferencesfirstPage>16</ReferencesfirstPage>
<ReferenceslastPage>19</ReferenceslastPage>
<References>REFERENCES
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Boehmer, E., Fong, K., and; Wu, J. (2012, March). International evidence on algorithmic trading. AFA 2013 San Diego Meetings Paper.
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Hilbert, M., and; Darmon, D. (2020). How complexity and uncertainty grew with algorithmic trading. Entropy, 22(5), 499.
Menkveld, A. J., and; Jovanovic, B. (2010). Middlemen in limit order markets. 2010 Meeting Papers (No. 955), Society for Economic Dynamics.
RAMKUMAR, G. (2018). A study on the significance of algorithmic trading in Indian stock market.
Syamala, S. R., and; Wadhwa, K. (2020). Trading performance and market efficiency: Evidence from algorithmic trading. Research in International Business and Finance, 54, 101283.
Treleaven, P., Galas, M., and; Lalchand, V. (2013). Algorithmic trading review. Communications of the ACM, 56(11), 76–85.
Vezeris, D. T., Schinas, C. J., Kyrgos, T. S., Bizergianidou, V. A., and; Karkanis, I. P. (2019). Optimization of back-testing techniques in automated high-frequency trading systems using the d-Back-test PS method. Computational Economics, 1–80.
Yadav, Y. (2015). How algorithmic trading undermines efficiency in capital markets. Vand. L. Rev., 68, 1607.
Zhang, F. (2010). High-frequency trading, stock volatility, and price discovery. Available at SSRN 1691679.(Note: You had two identical entries — Zhang and; Frank (2010) and Zhang, F. (2010) — the second one is correct and included.)</References>
</References>
</Journal>
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